In [518]:
!pip3 install plotly==4.14.1
Requirement already satisfied: plotly==4.14.1 in /opt/conda/lib/python3.8/site-packages (4.14.1)
Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from plotly==4.14.1) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.8/site-packages (from plotly==4.14.1) (1.3.3)
In [519]:
import json
import plotly.figure_factory as ff
import plotly.graph_objects as go
import plotly.express as px
import plotly.offline as py
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=False)
In [520]:
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
import sklearn
import math
from datetime import datetime, date
from sklearn import preprocessing
from sklearn import datasets
from sklearn import utils
from sklearn import linear_model
from sklearn.metrics import *
from sklearn.preprocessing import *
from statsmodels.formula.api import ols
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
In [521]:
import pandas as pd
import numpy as np
facebook = pd.read_csv("data/Facebook.csv", sep=',')
apple = pd.read_csv("data/Apple.csv", sep=',')
amazon = pd.read_csv("data/Amazon.csv", sep=',')
netflix = pd.read_csv("data/Netflix.csv", sep=',')
google = pd.read_csv("data/Google.csv", sep=',')
In [522]:
facebook['Date'] = pd.to_datetime(facebook['Date'])
apple['Date'] = pd.to_datetime(apple['Date'])
amazon['Date'] = pd.to_datetime(amazon['Date'])
netflix['Date'] = pd.to_datetime(netflix['Date'])
google['Date'] = pd.to_datetime(google['Date'])
In [523]:
facebook = facebook[(facebook['Date'].dt.year > 2012) & (facebook['Date'].dt.year < 2021)]
apple = apple[(apple['Date'].dt.year > 2012) & (apple['Date'].dt.year < 2021)]
amazon = amazon[(amazon['Date'].dt.year > 2012) & (amazon['Date'].dt.year < 2021)]
netflix = netflix[(netflix['Date'].dt.year > 2012) & (netflix['Date'].dt.year < 2021)]
google = google[(google['Date'].dt.year > 2012) & (google['Date'].dt.year < 2021)]

facebook = facebook.reset_index(drop=True)
apple = apple.reset_index(drop=True)
amazon = amazon.reset_index(drop=True)
netflix = netflix.reset_index(drop=True)
google = google.reset_index(drop=True)

facebook
Out[523]:
Date Open High Low Close Adj Close Volume
0 2013-01-02 27.440001 28.180000 27.420000 28.000000 28.000000 69846400
1 2013-01-03 27.879999 28.469999 27.590000 27.770000 27.770000 63140600
2 2013-01-04 28.010000 28.930000 27.830000 28.760000 28.760000 72715400
3 2013-01-07 28.690001 29.790001 28.650000 29.420000 29.420000 83781800
4 2013-01-08 29.510000 29.600000 28.860001 29.059999 29.059999 45871300
... ... ... ... ... ... ... ...
1916 2020-08-12 258.970001 263.899994 258.109985 259.890015 259.890015 21428300
1917 2020-08-13 261.549988 265.160004 259.570007 261.299988 261.299988 17374000
1918 2020-08-14 262.309998 262.649994 258.679993 261.239990 261.239990 14792700
1919 2020-08-17 262.500000 264.100006 259.399994 261.160004 261.160004 13351100
1920 2020-08-18 260.950012 265.149994 259.260010 262.339996 262.339996 18677500

1921 rows × 7 columns

In [524]:
df_corr = pd.DataFrame()

df_corr['Facebook'] = facebook['Close']
df_corr['Apple'] = apple['Close']
df_corr['Amazon'] = amazon['Close']
df_corr['Netflix'] = netflix['Close']
df_corr['Google'] = google['Close']

retscomp = df_corr.pct_change()

corr = retscomp.corr()
corr
Out[524]:
Facebook Apple Amazon Netflix Google
Facebook 1.000000 0.444546 0.505884 0.345712 0.562611
Apple 0.444546 1.000000 0.431872 0.250707 0.522914
Amazon 0.505884 0.431872 1.000000 0.439284 0.601770
Netflix 0.345712 0.250707 0.439284 1.000000 0.413904
Google 0.562611 0.522914 0.601770 0.413904 1.000000
In [525]:
fig = px.imshow(corr)
iplot(fig,show_link=False)
In [526]:
corr_df_fb = facebook[['Open', 'Close', 'High', 'Low', 'Adj Close', 'Volume']].copy(deep=True)

retscomp_fb = corr_df_fb.pct_change()

corr_fb = retscomp_fb.corr()
corr_fb
Out[526]:
Open Close High Low Adj Close Volume
Open 1.000000 0.401758 0.769315 0.758697 0.401758 0.016311
Close 0.401758 1.000000 0.747093 0.732999 1.000000 0.007707
High 0.769315 0.747093 1.000000 0.790089 0.747093 0.192635
Low 0.758697 0.732999 0.790089 1.000000 0.732999 -0.178854
Adj Close 0.401758 1.000000 0.747093 0.732999 1.000000 0.007707
Volume 0.016311 0.007707 0.192635 -0.178854 0.007707 1.000000
In [527]:
facebook['Company'] = ['Facebook']*len(facebook)
apple['Company'] = ['Apple']*len(apple)
amazon['Company'] = ['Amazon']*len(amazon)
netflix['Company'] = ['Netflix']*len(netflix)
google['Company'] = ['Google']*len(google)

frames = [facebook, apple, amazon, netflix, google]

result = pd.concat(frames)

fig = go.Figure()

fig.add_trace(go.Scatter(x=facebook.Date, y=facebook.Close, name='FB'))
fig.add_trace(go.Scatter(x=apple.Date, y=apple.Close, name='AAPL'))
fig.add_trace(go.Scatter(x=amazon.Date, y=amazon.Close, name='AMZN'))
fig.add_trace(go.Scatter(x=netflix.Date, y=netflix.Close, name='NFLX'))
fig.add_trace(go.Scatter(x=google.Date, y=google.Close, name='GOOG'))

fig.update_layout(title='Close prices for All Companies from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Facebook',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, False]},
                          {'title': 'FB',
                           'showlegend':True}]),
             dict(label = 'Apple',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False]},
                          {'title': 'APPL',
                           'showlegend':True}]),
             dict(label = 'Amazon',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False]},
                          {'title': 'AMZN',
                           'showlegend':True}]),
             dict(label = 'Netflix',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False]},
                          {'title': 'NFLX',
                           'showlegend':True}]),
             dict(label = 'Google',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True]},
                          {'title': 'GOOG',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [528]:
result['Year'] = np.arange(len(result.index))
result['Date'] = pd.to_datetime(result['Date'])

for x, rows in result.iterrows():
    result.loc[x, 'Year'] = rows['Date'].year

comp = result.groupby(['Company', 'Year'])

vol_df = pd.DataFrame()
vol = []
company = []
year = []

x = 0

for key,val in comp:
    a,b = key
    company.append(a)
    year.append(b)
    vol.append(comp.get_group(key).mean()['Volume'])

vol_df['Company'] = company
vol_df['Year'] = year
vol_df['Volume Mean'] = vol

fig = go.Figure()

avg_vol = vol_df['Volume Mean'].mean()
stand_vol = vol_df['Volume Mean'].std()

vol_df['standard_vol'] = np.arange(len(vol_df.index))
vol_df = vol_df.reset_index(drop=True)

for x, rows in vol_df.iterrows():
    vol_df.loc[x, 'standard_vol'] = (rows['Volume Mean'] - avg_vol)/(stand_vol)
    
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Facebook']['Year'], y=vol_df[vol_df['Company'] == 'Facebook']['standard_vol'], name='FB'))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Apple']['Year'], y=vol_df[vol_df['Company'] == 'Apple']['standard_vol'], name='AAPL'))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Amazon']['Year'], y=vol_df[vol_df['Company'] == 'Amazon']['standard_vol'], name='AMZN'))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Netflix']['Year'], y=vol_df[vol_df['Company'] == 'Netflix']['standard_vol'], name='NFLX'))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Google']['Year'], y=vol_df[vol_df['Company'] == 'Google']['standard_vol'], name='GOOG'))

fig.update_layout(title='Standardized Volume for All Companies from Jan 2013 to Aug 2020 Grouped by Year',
                   xaxis_title='Date',
                   yaxis_title='Standard Volume')

iplot(fig,show_link=False)
In [529]:
avg_14 = facebook.Close.rolling(window=14, min_periods=1).mean()
avg_21 = facebook.Close.rolling(window=21, min_periods=1).mean()
avg_100 = facebook.Close.rolling(window=100, min_periods=1).mean()
In [530]:
x_fb = facebook['Date']
y_fb = facebook['Open']
z_fb = facebook['Close']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x_fb, y=y_fb, name='Open',
                         line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=z_fb, name = 'Close',
                         line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
                         line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
                         line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
                         line=dict(color='mediumorchid', width=1.5)))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Facebook']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Facebook']['Volume Mean']/200000, name='Volume (scaled)', 
                     marker_color='slategray', opacity=0.3))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Facebook']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Facebook']['Volume Mean'], name='Volume', 
                     marker_color='slategray', visible='legendonly'))


fig.update_layout(title='Open/Close prices and Volume for Facebook from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Open/Close/Volume')

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True, True, False]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Open Price',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, False, False, False]},
                          {'title': 'Open Price',
                           'showlegend':True}]),
             dict(label = 'Close Price',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False, False, False]},
                          {'title': 'Close Price',
                           'showlegend':True}]),
             dict(label = '14 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False, False, False]},
                          {'title': '14 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '21 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False, False, False]},
                          {'title': '21 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '100 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True, False, False]},
                          {'title': '100 Day Moving Average',
                           'showlegend':True}]),
            dict(label = 'Volume (not scaled)',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, True]},
                          {'title': '100 Day Moving Average',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [531]:
avg_14 = apple.Close.rolling(window=14, min_periods=1).mean()
avg_21 = apple.Close.rolling(window=21, min_periods=1).mean()
avg_100 = apple.Close.rolling(window=100, min_periods=1).mean()
In [532]:
x_ap = apple['Date']
y_ap = apple['Open']
z_ap = apple['Close']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x_ap, y=y_ap, name='Open',
                         line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_ap, y=z_ap, name = 'Close',
                         line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
                         line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
                         line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
                         line=dict(color='mediumorchid', width=1.5)))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Apple']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Apple']['Volume Mean']/3500000, name='Volume (scaled)', 
                     marker_color='slategray', opacity=0.3))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Apple']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Apple']['Volume Mean'], name='Volume', 
                     marker_color='slategray', visible='legendonly'))


fig.update_layout(title='Open/Close prices and Volume for Apple from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Open/Close/Volume')

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True, True, False]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Open Price',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, False, False, False]},
                          {'title': 'Open Price',
                           'showlegend':True}]),
             dict(label = 'Close Price',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False, False, False]},
                          {'title': 'Close Price',
                           'showlegend':True}]),
             dict(label = '14 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False, False, False]},
                          {'title': '14 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '21 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False, False, False]},
                          {'title': '21 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '100 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True, False, False]},
                          {'title': '100 Day Moving Average',
                           'showlegend':True}]),
            dict(label = 'Volume (not scaled)',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, True]},
                          {'title': 'Volume (not scaled)',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [533]:
avg_14 = amazon.Close.rolling(window=14, min_periods=1).mean()
avg_21 = amazon.Close.rolling(window=21, min_periods=1).mean()
avg_100 = amazon.Close.rolling(window=100, min_periods=1).mean()
In [534]:
x_am = amazon['Date']
y_am = amazon['Open']
z_am = amazon['Close']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x_am, y=y_am, name='Open',
                         line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_am, y=z_am, name = 'Close',
                         line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
                         line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
                         line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
                         line=dict(color='mediumorchid', width=1.5)))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Amazon']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Amazon']['Volume Mean']/2000, name='Volume (scaled)', 
                     marker_color='slategray', opacity=0.3))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Amazon']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Amazon']['Volume Mean'], name='Volume', 
                     marker_color='slategray', visible='legendonly'))


fig.update_layout(title='Open/Close prices and Volume for Amazon from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Open/Close/Volume')

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True, True, False]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Open Price',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, False, False, False]},
                          {'title': 'Open Price',
                           'showlegend':True}]),
             dict(label = 'Close Price',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False, False, False]},
                          {'title': 'Close Price',
                           'showlegend':True}]),
             dict(label = '14 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False, False, False]},
                          {'title': '14 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '21 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False, False, False]},
                          {'title': '21 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '100 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True, False, False]},
                          {'title': '100 Day Moving Average',
                           'showlegend':True}]),
            dict(label = 'Volume (not scaled)',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, True]},
                          {'title': 'Volume (not scaled)',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [535]:
avg_14 = netflix.Close.rolling(window=14, min_periods=1).mean()
avg_21 = netflix.Close.rolling(window=21, min_periods=1).mean()
avg_100 = netflix.Close.rolling(window=100, min_periods=1).mean()
In [536]:
x_ne = netflix['Date']
y_ne = netflix['Open']
z_ne = netflix['Close']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x_ne, y=y_ne, name='Open',
                         line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_ne, y=z_ne, name = 'Close',
                         line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
                         line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
                         line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
                         line=dict(color='mediumorchid', width=1.5)))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Netflix']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Netflix']['Volume Mean']/50000, name='Volume (scaled)', 
                     marker_color='slategray', opacity=0.3))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Netflix']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Netflix']['Volume Mean'], name='Volume', 
                     marker_color='slategray', visible='legendonly'))


fig.update_layout(title='Open/Close prices and Volume for Netflix from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Open/Close/Volume')

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True, True, False]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Open Price',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, False, False, False]},
                          {'title': 'Open Price',
                           'showlegend':True}]),
             dict(label = 'Close Price',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False, False, False]},
                          {'title': 'Close Price',
                           'showlegend':True}]),
             dict(label = '14 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False, False, False]},
                          {'title': '14 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '21 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False, False, False]},
                          {'title': '21 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '100 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True, False, False]},
                          {'title': '100 Day Moving Average',
                           'showlegend':True}]),
            dict(label = 'Volume (not scaled)',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, True]},
                          {'title': 'Volume (not scaled)',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [537]:
avg_14 = google.Close.rolling(window=14, min_periods=1).mean()
avg_21 = google.Close.rolling(window=21, min_periods=1).mean()
avg_100 = google.Close.rolling(window=100, min_periods=1).mean()
In [538]:
x_go = google['Date']
y_go = google['Open']
z_go = google['Close']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x_go, y=y_go, name='Open',
                         line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_go, y=z_go, name = 'Close',
                         line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
                         line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
                         line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
                         line=dict(color='mediumorchid', width=1.5)))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Google']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Google']['Volume Mean']/2000, name='Volume (scaled)', 
                     marker_color='slategray', opacity=0.3))
fig.add_trace(go.Bar(x=vol_df[vol_df['Company'] == 'Google']['Year'], 
                     y=vol_df[vol_df['Company'] == 'Google']['Volume Mean'], name='Volume', 
                     marker_color='slategray', visible='legendonly'))


fig.update_layout(title='Open/Close prices and Volume for Google from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Open/Close/Volume')

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True, True, False]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Open Price',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, False, False, False]},
                          {'title': 'Open Price',
                           'showlegend':True}]),
             dict(label = 'Close Price',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False, False, False]},
                          {'title': 'Close Price',
                           'showlegend':True}]),
             dict(label = '14 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False, False, False]},
                          {'title': '14 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '21 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False, False, False]},
                          {'title': '21 Day Moving Average',
                           'showlegend':True}]),
             dict(label = '100 Day Moving Average',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True, False, False]},
                          {'title': '100 Day Moving Average',
                           'showlegend':True}]),
            dict(label = 'Volume (not scaled)',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, True]},
                          {'title': 'Volume (not scaled)',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [539]:
avg_close = result.groupby('Date')['Close'].mean()
stand_close = result.groupby('Date')['Close'].std()

stand_close = stand_close.reset_index()
avg_close = avg_close.reset_index()

result['standard_close'] = np.arange(len(result.index))
result = result.reset_index(drop=True)

for x, rows in result.iterrows():
    result.loc[x, 'standard_close'] = (rows['Close'] - avg_close[avg_close['Date'] == rows['Date']]['Close']).values/(stand_close[stand_close['Date'] == rows['Date']]['Close']).values
    
result
Out[539]:
Date Open High Low Close Adj Close Volume Company Year standard_close
0 2013-01-02 27.440001 28.180000 27.420000 28.000000 28.000000 69846400.0 Facebook 2013 -0.663215
1 2013-01-03 27.879999 28.469999 27.590000 27.770000 27.770000 63140600.0 Facebook 2013 -0.665516
2 2013-01-04 28.010000 28.930000 27.830000 28.760000 28.760000 72715400.0 Facebook 2013 -0.659137
3 2013-01-07 28.690001 29.790001 28.650000 29.420000 29.420000 83781800.0 Facebook 2013 -0.661599
4 2013-01-08 29.510000 29.600000 28.860001 29.059999 29.059999 45871300.0 Facebook 2013 -0.661829
... ... ... ... ... ... ... ... ... ... ...
9610 2020-08-31 1643.569946 1644.500000 1625.329956 1629.530029 1629.530029 1321100.0 Google 2020 0.707107
9611 2020-09-01 1632.160034 1659.219971 1629.530029 1655.079956 1655.079956 1133800.0 Google 2020 0.707107
9612 2020-09-02 1668.010010 1726.099976 1660.189941 1717.390015 1717.390015 2476100.0 Google 2020 NaN
9613 2020-09-03 1699.520020 1700.000000 1607.709961 1629.510010 1629.510010 3180200.0 Google 2020 NaN
9614 2020-09-04 1609.000000 1634.989990 1537.970093 1581.209961 1581.209961 2792533.0 Google 2020 NaN

9615 rows × 10 columns

In [540]:
fig = px.line(result, x="Date", y="standard_close", color='Company')

fig.update_layout(title='Standardized Close prices for All Companies from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Standardized Close Price')

iplot(fig,show_link=False)
In [541]:
facebook['timestamp'] = pd.to_datetime(facebook.Date).astype(int) // (10**9)
X = np.array(facebook['timestamp']).reshape(-1,1)
y = np.array(facebook['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = go.Figure()

fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))

model = KNeighborsRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))

model = DecisionTreeRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Linear Regression',
                  method = 'update',
                  args = [{'visible': [True, True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Regression Line Fit for Facebook from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [542]:
apple['timestamp'] = pd.to_datetime(apple.Date).astype(int) // (10**9)
X = np.array(apple['timestamp']).reshape(-1,1)
y = np.array(apple['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = go.Figure()

fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))

model = KNeighborsRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))

model = DecisionTreeRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Linear Regression',
                  method = 'update',
                  args = [{'visible': [True, True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Regression Line Fit for Apple from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [543]:
amazon['timestamp'] = pd.to_datetime(amazon.Date).astype(int) // (10**9)
X = np.array(amazon['timestamp']).reshape(-1,1)
y = np.array(amazon['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = go.Figure()

fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))

model = KNeighborsRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))

model = DecisionTreeRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Linear Regression',
                  method = 'update',
                  args = [{'visible': [True, True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Regression Line Fit for Amazon from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [544]:
netflix['timestamp'] = pd.to_datetime(netflix.Date).astype(int) // (10**9)
X = np.array(netflix['timestamp']).reshape(-1,1)
y = np.array(netflix['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = go.Figure()

fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))

model = KNeighborsRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))

model = DecisionTreeRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Linear Regression',
                  method = 'update',
                  args = [{'visible': [True, True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Regression Line Fit for Netflix from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [545]:
google['timestamp'] = pd.to_datetime(google.Date).astype(int) // (10**9)
X = np.array(google['timestamp']).reshape(-1,1)
y = np.array(google['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = go.Figure()

fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))

model = KNeighborsRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))

model = DecisionTreeRegressor()
model.fit(X_train, y_train)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Linear Regression',
                  method = 'update',
                  args = [{'visible': [True, True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Regressor',
                  method = 'update',
                  args = [{'visible': [True, True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Regression Line Fit for Google from Jan 2013 to Aug 2020',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [546]:
df = facebook[['Close']].copy(deep=True)

future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)

X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)

x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days) 
x_future = np.array(x_future)

tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)

predictions = tree_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
                         line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
                         line=dict(width=1.5)))

predictions = lr_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
                         line=dict(width=1.5)))

predictions = knn_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
                         line=dict(width=1.5)))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Prediction',
                  method = 'update',
                  args = [{'visible': [True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'Linear Regression Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Predicted Values for Facebook For the last 500 Days',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [547]:
df = apple[['Close']].copy(deep=True)

future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)

X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)

x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days) 
x_future = np.array(x_future)

tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)

predictions = tree_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
                         line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
                         line=dict(width=1.5)))

predictions = lr_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
                         line=dict(width=1.5)))

predictions = knn_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
                         line=dict(width=1.5)))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Prediction',
                  method = 'update',
                  args = [{'visible': [True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'Linear Regression Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Predicted Values for Apple For the last 500 Days',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [548]:
df = amazon[['Close']].copy(deep=True)

future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)

X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)

x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days) 
x_future = np.array(x_future)

tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)

predictions = tree_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
                         line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
                         line=dict(width=1.5)))

predictions = lr_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
                         line=dict(width=1.5)))

predictions = knn_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
                         line=dict(width=1.5)))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Prediction',
                  method = 'update',
                  args = [{'visible': [True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'Linear Regression Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Predicted Values for Amazon For the last 500 Days',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [549]:
df = netflix[['Close']].copy(deep=True)

future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)

X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)

x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days) 
x_future = np.array(x_future)

tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)

predictions = tree_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
                         line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
                         line=dict(width=1.5)))

predictions = lr_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
                         line=dict(width=1.5)))

predictions = knn_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
                         line=dict(width=1.5)))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Prediction',
                  method = 'update',
                  args = [{'visible': [True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'Linear Regression Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Predicted Values for Netflix For the last 500 Days',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [550]:
df = google[['Close']].copy(deep=True)

future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)

X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)

x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days) 
x_future = np.array(x_future)

tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)

predictions = tree_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
                         line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
                         line=dict(width=1.5)))

predictions = lr_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
                         line=dict(width=1.5)))

predictions = knn_prediction
valid =  df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]

fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
                         line=dict(width=1.5)))

fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label = 'All',
                  method = 'update',
                  args = [{'visible': [True, True, True, True]},
                          {'title': 'All',
                           'showlegend':True}]),
             dict(label = 'Decision Tree Prediction',
                  method = 'update',
                  args = [{'visible': [True, True, False, False]},
                          {'title': 'Linear Regression',
                           'showlegend':True}]),
             dict(label = 'Linear Regression Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, True, False]},
                          {'title': 'k-NN Regressor',
                           'showlegend':True}]),
             dict(label = 'k-NN Regressor Prediction',
                  method = 'update',
                  args = [{'visible': [True, False, False, True]},
                          {'title': 'Decision Tree Regressor',
                           'showlegend':True}]),
            ]), 
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_layout(title='Predicted Values for Google For the last 500 Days',
                   xaxis_title='Date',
                   yaxis_title='Close Price')

fig.update_layout(
    autosize=False,
    width=1000,
    height=650,)

iplot(fig,show_link=False)
In [ ]: